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Made it consistent across the README
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Will Stanton 2015-07-22 10:37:49 -06:00
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README.md
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@ -6,7 +6,7 @@ DMLC/XGBoost
An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version.
It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data
Checkout our [Committers and Contributors](CONTRIBUTORS.md) who help make xgboost better.
Check out our [Committers and Contributors](CONTRIBUTORS.md) who help make xgboost better.
Documentation: [Documentation of dmlc/xgboost](doc/README.md)
@ -27,7 +27,7 @@ XGBoost is part of [Distributed Machine Learning Common](http://dmlc.github.io/)
What's New
==========
* XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance)
- Checkout the winning solution at [Highlight links](doc/README.md#highlight-links)
- Check out the winning solution at [Highlight links](doc/README.md#highlight-links)
* XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04)
* XGBoost helps three champion teams to win [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing)
- Check out the winning solution at [Highlight links](doc/README.md#highlight-links)